Average throughput maximization for energy harvesting transmitters with causal energy arrival information

We consider an energy harvesting node which transmits data using the energy it harvests from the environment. In the simple scenario of point-to-point communication, the performance of the node in terms of throughput is greatly influenced by the transmission strategy it employs and its knowledge about the energy arriving process. We assume in this work that the transmitting node does not have non-causal information about the energy to be harvested in future, but only has available the statistics of the energy arriving process which is stationary. The practical considerations that the energy storage capacity of the node is limited and there is additional energy consumption within the circuitry of the node are taken into account by our system model. Viewing the system as a finite-state Markov decision process, we optimize the transmission policy the node employs as a function of the energy storage state after an energy arrival, by using the policy-iteration algorithm. The asymptotic performance of the system in terms of average throughput is studied within the established theoretical and algorithmic framework, under the several transmission strategies we propose. Simulation results indicate the advantage of each strategy with respect to a certain range of values that the system parameters may take.